<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>32311</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>157</pageFirst>
    <pageLast>176</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Singapor</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-01-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Unsupervised Optimization of Laser Beam Trajectories for Powder Bed Fusion Printing and Extension to Multiphase Nucleation Models</title>
    <abstract language="eng">In laser powder bed fusion, it is known that the quality of printing results crucially depends on the temperature distribution and its gradient over the manufacturing plate. We propose a computational model for the motion of the laser beam and the simulation of the time-dependent heat evolution over the plate. For the optimization of the laser beam trajectory, we propose a cost function that minimizes the average thermal gradient and allows to steer the laser beam. The optimization is performed in an unsupervised way. Specifically, we propose an optimization heuristic that is inspired by the well-known traveling salesman problem and that employs simulated annealing to determine a nearly optimal pathway. By comparison of the heat transfer simulations of the derived trajectories with trajectory patterns from standard printing protocols we show that the method gives superior results in terms of the given cost functional.</abstract>
    <parentTitle language="eng">Mathematical Methods for Objects Reconstruction : From 3D Vision to 3D Printing</parentTitle>
    <identifier type="isbn">978-981-99-0775-5</identifier>
    <identifier type="issn">978-981-99-0776-2</identifier>
    <identifier type="doi">10.1007/978-981-99-0776-2_6</identifier>
    <enrichment key="UBICOseries">Springer INdAM Series (SINDAMS) ; volume 54</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Ashkan</firstName>
      <lastName>Mansouri Yarahmadi</lastName>
    </author>
    <submitter>
      <firstName>Annette</firstName>
      <lastName>Kallweit</lastName>
    </submitter>
    <author>
      <firstName>Michael</firstName>
      <lastName>Breuß</lastName>
    </author>
    <author>
      <firstName>Carsten</firstName>
      <lastName>Hartmann</lastName>
    </author>
    <author>
      <firstName>Toni</firstName>
      <lastName>Schneidereit</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Additive manufacturing     Multiphase alloys     Trajectory optimization     Powder bed fusion printing     Heat simulation     Linear-quadratic control</value>
    </subject>
    <collection role="institutes" number="1303">FG Angewandte Mathematik</collection>
  </doc>
</export-example>
